A Generalized Model for Wind Turbine Faulty Condition Detection Using Combination Prediction Approach and Information Entropy

A generalized model for detecting the incipient wind turbine (WT) faulty condition based on the data collected from wind farm supervisory control and data acquisition (SCADA) system is proposed in this paper. The linear combination prediction approach and the information entropy are integrated to develop the generalized model, in which the linear combination prediction approach improves the accuracy and generalization performance of the model, and the information entropy of prediction residual quantifies the abnormal level of the condition parameter. SCADA datasets were selected to establish the prediction models of WT condition parameters that are dependent on environmental conditions such as ambient temperature and wind speed. The combination prediction models of WT condition parameters were developed based on different data mining algorithms such as Back propagation neural network (BPNN) algorithm, radial basis function neural network (RBFNN) algorithm and least square support vector machine (LSSVM) algorithm. The information entropy was utilized to extract useful information from residuals of the prediction models for WT faulty condition detection. Finally, the proposed method has been used for real 1.5 MW WTs with doubly fed induction generators (DFIG). Through investigation of cases of actual WT faults, the effectiveness of the proposed WT imminent fault identification approach was verified.

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